Advancements in Genetic
Engineering
Tang, et al., Adv Genet Eng 2014, 3:1
http://dx.doi.org/10.4172/2169-0111.1000e113
Editorial
Open Access
Pleiotropic Enrichment Analysis with Diverse Omics Data
Wenlong Tang1*, Jigang Zhang2,3 and Dongdong Lin2,4
1Department
2Center
of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, USA
of Genomics and Bioinformatics, Tulane University, New Orleans, LA, USA
3Department
of Biostatistics and Bioinformatics, Tulane University, New Orleans, LA, USA
4Department
of Biomedical Engineering, Tulane University, New Orleans, LA, USA
*Corresponding
author: Wenlong Tang, Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL, 35487, USA, Tel: 504-988-1341;
E-mail: wtang11@bama.ua.edu
Rec date: 31 Oct, 2014; Acc date: 03 Nov, 2014; Pub date: 05 Nov, 2014
Copyright: © 2014 Tang W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Introduction
In human complex diseases, the phenomenon that a genetic variant
or gene can affect multiple diseases (or phenotypes) is referred as
“Pleiotropy” [1]. Pleiotropy is usually considered as the most
important source for genetic correlation between traits [2]. The
pleiotropy highlights the fact that some genes in human genome
perform multiple biological functions. For example, epidemiological
studies indicate that individuals with increased genetic susceptibility to
diabetes have the decreased risk of prostate cancer [3,4]. The
association analysis between these two diseases show that several genes
can affect both type 2 diabetes and prostate cancer risk via pleiotropy
[3,5]. The identification and characterization of pleiotropic genes can
offer a unique way to dissect complex etiology of these diseases.
Although examples of pleiotropic genes have been discovered in
human complex diseases [6], pleiotropy remains a poorly understood
and there have been very few systematic studies [7]. Recently
systematic phenome-wide association studies (PheWASs) have been
proposed, in which a SNP with an established association with a
phenotype is screened for association with hundreds of other
phenotypes [8,9]. Compared with single-trait analysis, the multipletrait analysis in PheWASs can combine the information from
genetically correlated traits to identify pleiotropic genes associated
with disease risk [10]. Genome-wide association studies with an
abundance of genetic variants and various traits have provided an
opportunity to fully examine pleiotropy in a systematic manner for the
human complex diseases. For example, numerous approaches
extended the regression framework for testing the association between
a genetic variant and multiple correlated phenotypes, using variations
of generalized estimating equations [11]. Other approaches included a
dimension reduction technique, e.g. Principal components analysis, on
the phenotypes before testing the association with the genetic variant
in a genetic association analysis [12]. There were also approaches to
combine the association results across various phenotypes by metaanalysis to identify those variants that are associated with multiple
phenotypes [13].
But these methods mainly focused on the detection of single
pleiotropic SNPs or genes in single omics data. It is increasingly
recognized that complex diseases result from the joint effects of
multiple genes which are from the same biological pathways [14,15].
Genes that belong to the same pathway might have a similar pattern of
pleiotropic effects. Therefore, only testing pleiotropy of single SNPs or
genes is insufficient to dissect the genetic structures of complex
diseases. Additionally, identification of pleiotropic genes for complex
diseases is limited by the relatively modest effects of genetic variants
[16]. Thus it is especially meaningful to identify groups of pleiotropic
Adv Genet Eng
ISSN:2169-0111 AGE, an open access Journal
pathways associated with the traits of interest, in which there are
enrichment of pleiotropic genes.
In the past few years, the availability of high-throughput techniques
can lead to the opportunity to generate different omics data
(genomics, transcriptomics, epigenomics, and proteomics data) for
systematic analysis of genome-wide levels of pleiotropy at multi-omics
levels. Characterizing the underlying biological mechanism of a
pleiotropic effect is a big challenge with multi-omics data. Systemslevel approaches must be developed to detect pleiotropic variants and
pathways (network modules). We aim to gain insight into the extent
and pattern of pleiotropy in the genetics of complex disease to
characterize pleiotropic genes for clusters of diseases and disease traits.
Here we present a Pleiotropic Enrichment Analysis (PEA) for
multi-omics data to identify pleiotropic functional modules and genes
underlying multiple genetically related diseases as following:
First, systematically identify pleiotropic genes. We can compute
crosstalk scores by combining the pleiotropic evidence of genes
obtained from individual uni-omics studies through a meta-analysis
based approach [17].
Second, perform pathway-enrichment analysis with Signaling
Pathway Impact Analysis to identify the pathway enriched with
pleiotropic genes [18], which calculates the significance of a pathway
according to both the over-representation evidence and perturbationbased evidence using the topology of the network.
Third, incorporate the prior knowledge for pleiotropic network
reconstruction. This strategy incorporates the information of prior
knowledge (e.g. protein-protein interaction network and online
databases for miRNA target prediction) into pathway enriched with
pleiotropic genes to rebuild pleiotropic regulation modules [19,20].
Overall, we provide a general guideline for detection of pleiotropy
using multi-omics data by PEA. The identification and
characterization of pleiotropy is crucial for a comprehensive biological
understanding of complex disease. As genetic information from
different omics data is increasingly integrated into medical practice,
characterizing pleiotropic effects may improve the accuracy of these
genetic tests and the interpretation of results on diseases. The existence
of pleiotropic pathways (or modules) in distinct disorders may suggest
new opportunities and challenges for drug discovery. Drugs developed
for one disorder could be repurposed to treat another disorder if the
therapeutic target genetic factors is common to the biology of both
disorders. Furthermore, we anticipate that the pleiotropic pathways/
modules might give clues to underlying common mechanisms on
genetically correlated diseases.
Volume 3 • Issue 1 • 1000e113
Citation:
Tang W, Zhang J, Lin D (2014) Pleiotropic Enrichment Analysis with Diverse Omics Data. Adv Genet Eng 3: e113. doi:
10.4172/2169-0111.1000e113
Page 2 of 2
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